In a collaboration with GATAN Inc., we have reported the development of a novel, multi-specimen imaging system for high-throughput transmission electron microscopy that circumvents time-consuming steps involved in manual specimen loading. Bob Morrison and I designed the system; all of the hardware construction was done at Gatan UK, while all of the software was carried out in my lab. This cartridge-based loading system, called the Gatling, permits the sequential examination of as many as 100 specimens in the microscope for room temperature electron microscopy using mechanisms for rapid and automated specimen exchange. The software for the operation of the Gatling and automated data acquisition has been implemented in an updated version of our in-house program AutoEM. In the current implementation of the system, the time required to deliver 95 specimens into the microscope and collect overview images from each is about 13 hours. Regions of interest are identified from a low magnification atlas generation from each specimen and an unlimited number of higher magnifications images can be subsequently acquired from these regions using fully automated data acquisition procedures that can be controlled from a remote interface. We anticipate that the availability of the Gatling will greatly accelerate the speed of data acquisition for a variety of applications in biology, materials science and nanotechnology that require rapid screening and image analysis of multiple specimens. Strategies for the determination of 3D structures of biological macromolecules using electron crystallography and single particle electron microscopy utilize powerful tools for the averaging of information obtained from 2D projection images of structurally homogeneous specimens. In contrast, electron tomographic approaches have often been used to study the 3D structures of heterogeneous, one-of-a-kind objects such as whole cells where image averaging strategies are not applicable. Complex entities such as cells and viruses, nevertheless, contain multiple copies of numerous macromolecules that can individually be subjected to 3D averaging. We have deeloped a complete framework for alignment, classification, and averaging of volumes derived by electron tomography that is computationally efficient and effectively accounts for the missing wedge that is inherent to limited angle electron tomography. Modeling the missing data as a multiplying mask in reciprocal space we have shown that the effect of the missing wedge can be accounted for seamlessly in all alignment and classification operations. We solve the alignment problem using the convolution theorem in harmonic analysis, thus eliminating the need for approaches that require exhaustive angular search, and adopt an iterative approach to alignment and classification that does not require the use of external references. We also demonstrated that our method could be successfully applied for 3D classification and averaging of phantom volumes as well as experimentally obtained tomograms of GroEL where the outcomes of the analysis can be quantitatively compared against the expected results. Another area of focus has been on image processing and segmentation. Previous studies using nonlinear anisotropic methods, wavelet based methods and filtering have already demonstrated the value of image denoising in various 2D and 3D datasets. The existing methods usually consider clean data (or assume that clean data is available) and artificially add different types of noise to the clean data and then denoise the noisy data assuming that the statistics of noise is known using various algorithms. Furthermore, a single denoising algorithm may not perform uniformly well on diverse datasets that have been collected using a variety of specimens and acquisition conditions due to variations in noise patterns. Variations may occur due to low radiation, a lot of biological structure, errors in alignment and reconstruction and the presence of the missing wedge. We have investigated the use of transform-domain denoising techniques and feature extraction to improve quantitative interpretation of cryo electron tomograms of viruses and cells. We have evaluated the relative merits of a variety of denoising algorithms on the detection, clustering, and computation of the spatial distribution of specific macromolecular complexes. As opposed to most previous denoising analyses, we are working with 3D volumes that have a finite missing wedge of information in reciprocal space, and we have neither an apriori knowledge of the type of noise nor its statistics. Moreover, quantitative analysis of denoising algorithms using goodness-of-fit (GOF) criteria as we implemented have not yet been applied, to our knowledge, for noise analysis in electron tomography. In our approach, we have used four metrics for analysis including the Kullback-Leibler (KL) distance based GOF test, Fourier ring correlation and single-image SNR to iteratively obtain the optimal denoising algorithm for a given 3D volume. Using these methods, we show that denoising, when used with care is an enormously powerful tool for the automated interpretation of complex 3D data sets at high throughput